In [3]:

    

import numpy as np
import matplotlib.pyplot as plt


    

In [4]:

    

x = np.linspace(0, 1, 500)
y = np.sin(4 * np.pi * x) * np.exp(-5 * x)

fig, ax = plt.subplots()

ax.fill(x, y, zorder=10)
ax.grid(True, zorder=5)
plt.show()


    

In [5]:

    

from matplotlib import mlab

mu = 200
sigma = 25
n_bins = 50
x = mu + sigma*np.random.randn(10000)

n, bins, patches = plt.hist(x, n_bins, normed=1,
                            histtype='step', cumulative=True)

# Add a line showing the expected distribution.
y = mlab.normpdf(bins, mu, sigma).cumsum()
y /= y[-1]
plt.plot(bins, y, 'k--', linewidth=1.5)

# Overlay a reversed cumulative histogram.
plt.hist(x, bins=bins, normed=1, histtype='step', cumulative=-1)

plt.grid(True)
plt.title('cumulative step')

plt.show()


    

In [6]:

    

from matplotlib.ticker import NullFormatter

np.random.seed(1)
# make up some data in the interval ]0, 1[
y = np.random.normal(loc=0.5, scale=0.4, size=1000)
y = y[(y > 0) & (y < 1)]
y.sort()
x = np.arange(len(y))

# plot with various axes scales
fig, axs = plt.subplots(2, 2, sharex=True)
fig.subplots_adjust(left=0.08, right=0.98, wspace=0.3)

# linear
ax = axs[0, 0]
ax.plot(x, y)
ax.set_yscale('linear')
ax.set_title('linear')
ax.grid(True)


# log
ax = axs[0, 1]
ax.plot(x, y)
ax.set_yscale('log')
ax.set_title('log')
ax.grid(True)


# symmetric log
ax = axs[1, 1]
ax.plot(x, y - y.mean())
ax.set_yscale('symlog', linthreshy=0.02)
ax.set_title('symlog')
ax.grid(True)

# logit
ax = axs[1, 0]
ax.plot(x, y)
ax.set_yscale('logit')
ax.set_title('logit')
ax.grid(True)
ax.yaxis.set_minor_formatter(NullFormatter())


plt.show()